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DICErClust  

Deep Significance Clustering for Clinical Risk Stratification
View on CRAN: Click here


Download and install DICErClust package within the R console
Install from CRAN:
install.packages("DICErClust")

Install from Github:
library("remotes")
install_github("cran/DICErClust")

Install by package version:
library("remotes")
install_version("DICErClust", "0.1.2")



Attach the package and use:
library("DICErClust")
Maintained by
Sarah Ayton
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-05-28
Latest Update: 2026-05-28
Description:
We provide an R implementation of Deep Significance Clustering (DICE), a self-supervised learning framework designed to identify clinically meaningful and risk-stratified patient subgroups from electronic health record (EHR) data. DICE jointly optimizes deep representation learning, clustering, and outcome prediction while enforcing statistical significance between predicted outcomes and cluster membership. This integrated optimization produces subgroups that are both clinically coherent and predictive, addressing a gap where traditional unsupervised clustering methods and supervised risk prediction models alone may fail to generate actionable clinical groupings. See Huang et al. (2021) <doi:10.1093/jamia/ocab203>.
How to cite:
Sarah Ayton (2026). DICErClust: Deep Significance Clustering for Clinical Risk Stratification. R package version 0.1.2, https://cran.r-project.org/web/packages/DICErClust. Accessed 27 Jun. 2026.
Previous versions and publish date:
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Complete documentation for DICErClust
Functions, R codes and Examples using the DICErClust R package
Full DICErClust package functions and examples
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